Introduction

The aim is to explore the annotations and label transfers for all of the samples in SCPCP000006.

In order to explore the label transfer results, we look into some marker genes, table and percentages of cells in each annotation groups (from label transfers).

Packages

library(Seurat)
library(sctransform)
library(SCpubr)             # for plotting 
library(tidyverse)
library(patchwork)

library(reshape2)
library(org.Hs.eg.db)

set.seed(params$seed)

Base directories

# The base path for the OpenScPCA repository, found by its (hidden) .git directory
repository_base <- rprojroot::find_root(rprojroot::is_git_root)

# The current data directory, found within the repository base directory
data_dir <- file.path(repository_base, "data", "current", params$scpca_project_id)

# The path to this module
module_base <- file.path(repository_base, "analyses", "cell-type-wilms-tumor-06")

result_dir <- file.path(module_base, "results")

Input files

In this notebook, we are working with all of the samples in SCPCP000006.

We work with the pre-processed and labeled Seurat object that is the output of 02b_label-transfer_fetal_kidney_reference_Stewart.Rmd saved in the results directory.

The sample metadata can be found in sample_metadata_file in the data folder.

sample_metadata_file <- file.path(repository_base, "data", "current", params$scpca_project_id, "single_cell_metadata.tsv")

metadata <- read.table(sample_metadata_file, sep = "\t", header = TRUE)

To explore the annotation results, we look into some marker genes reported here:

CellType_metadata <- read_csv(file.path(module_base, "marker-sets", "CellType_metadata.csv"))

DT::datatable(CellType_metadata, caption = ("CellType_metadata"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',
                               buttons = c( 'csv', 'excel')))

Output file

The report will be saved in the notebook directory.

Functions

Here we defined function that will be used multiple time all along the notebook.

Visualize seurat clusters and metadata

For a Seurat object objectand a metadata metadata, the function visualize_metadata will plot FeaturePlot and BarPlot

  • object is the Seurat object

  • metadata the gene or quantitative value to be plotted

  • group.by is the metadata used for grouping the violin plots

visualize_metadata <- function(object, meta, group.by){
  
  if(is.numeric(object@meta.data[,meta])){
     d <- SCpubr::do_FeaturePlot(object, 
                                              features = meta, 
                                              reduction = "umap.rpca",
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(meta)
    b <- SCpubr::do_ViolinPlot(srat, 
                                             features = meta, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none")
                   
   return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
  }
  
  
  else{
    
  
  d <- SCpubr::do_DimPlot(object, reduction="umap.rpca", group.by = meta, label = TRUE, repel = TRUE) + ggtitle(paste0(meta," - umap")) + theme(text=element_text(size=18)) + NoLegend()
  b <- SCpubr::do_BarPlot(sample = object,
                         group.by = meta,
                         split.by = group.by,
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(print(group.by)) +
                         theme(text=element_text(size=18))
  return(d + b + plot_layout(ncol = 1, heights = c(4,2)))
  }
  
}

Visualize seurat clusters and markers genes

For a Seurat object objectand a features features, the function visualize_feature will plot FeaturePlot and ViolinPlot

  • object is the Seurat object

  • features the gene or quantitative value to be plotted

  • group.by is the metadata used for grouping the violin plots

visualize_feature <- function(object, features, group.by = "seurat_clusters"){
 
                  feature_symbol <- AnnotationDbi::select(org.Hs.eg.db, 
                                keys=features, 
                                columns="SYMBOL", 
                                keytype="ENSEMBL")
 
                  d <- SCpubr::do_FeaturePlot(object, 
                                              features = feature_symbol$ENSEMBL,
                                              reduction = "umap.rpca",
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(feature_symbol$SYMBOL)
                  b <- SCpubr::do_ViolinPlot(srat, 
                                             features = feature_symbol$ENSEMBL, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none",
                                             assay = "SCT") + ylab(feature_symbol$SYMBOL)
                   
                  return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
}

do_Table_Heatmap

do_Table_Heatmap shows heatmap of counts of cells for combinations of two metadata variables

  • data seurat object
  • first_group is the name of the first metadata to group the cells
  • last_group is the name of the second metadata to group the cells
do_Table_Heatmap <- function(data, first_group, last_group ){
  
  df <- data@meta.data %>%
  mutate_if(sapply(data@meta.data, is.character), as.factor)  %>% 
  group_by( !!sym(first_group), !!sym(last_group))%>% 
  summarise(Nb = log(n())) 

p <- ggplot(df, aes(x= !!sym(first_group), y = !!sym(last_group), fill = Nb)) +
   geom_tile() +
   scale_fill_viridis_c()  +
   theme_bw() +
   theme(text = element_text(size = 20)) +
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
   guides(fill=guide_legend(title=" Number of \n cells (log)"))

return(p)
}

Analysis

Load the pre-processed Seurat object

# open the processed rds object
filelist <- list.files(result_dir, 
                       recursive = TRUE, 
                       pattern = "02b-fetal_kidney_label-transfer_", 
                       full.names = TRUE)

sample_id <- list.files(result_dir, 
                       recursive = TRUE, 
                       pattern = "02b-fetal_kidney_label-transfer_", 
                       full.names = FALSE)
sample_id <- colsplit(sample_id, pattern = "/", names = c("sample_id", "suffixe"))
sample_id <- sample_id[,1]

sce <- lapply(filelist, function(x) readRDS(x))

Merge Seurat objects (with integration)

We followed the standard Seurat workflow described here to merge and integrate the 40 samples from the Wilms tumor dataset SCPCP000006. For more information, see the vignette Merge objects (with integration). We have slight changes as we already performed normalization using SCTransform.

options(future.globals.maxSize= 8912896000000)

srat <- merge(sce[[1]], y = sce[2:length(sce)], 
              add.cell.ids = sample_id, 
              project = params$scpca_project_id)

DefaultAssay(object = srat) <- "SCT"

VariableFeatures(srat[["SCT"]]) <- rownames(srat[["SCT"]]@scale.data) 

srat <- RunPCA(srat)

srat <- IntegrateLayers(object = srat, 
                        method = RPCAIntegration,
                        orig.reduction = "pca", 
                        new.reduction = 'rpca',
                        normalization.method="SCT", verbose = FALSE)

srat <- FindNeighbors(srat, dims = 1:30, reduction = "rpca")
srat <- FindClusters(srat)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 200338
## Number of edges: 7344017
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8989
## Number of communities: 42
## Elapsed time: 135 seconds
srat <- RunUMAP(srat, reduction = "rpca", dims = 1:30, reduction.name = "umap.rpca")

Quick look at the integration

This is not the main point of this notebook, we expect to work on integration later on, one of the final step of the analysis. We simply here would like to have a rough idea of cell/sample distribution.

tmp <- colsplit(colnames(srat), pattern = "_", names = c("sample_id", "barcode"))
srat <- AddMetaData(srat, tmp$sample_id, col.name = "sample_id")

d1 <- SCpubr::do_DimPlot(srat, reduction="umap.rpca", group.by = "sample_id", label = FALSE) + ggtitle("sample_id - umap.rpca reduction")

d2 <- SCpubr::do_DimPlot(srat, reduction="umap.rpca", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("seurat_clusters - umap.rpca reduction")


d1 | d2

Cell annotation

Introduction to the four annotation strategies

The use of the right reference is crucial for label transfer and annotation. It is recommended that the cell types in the reference is representative to the cell types to be annotated in the query.

Wilms tumors can contain up to three histologies that resemble fetal kidney: blastema, stroma, and epithelia [1, 2]. Because of their histological similarity to fetal kidneys, Wilms tumors are thought to arise from developmental derangements in embryonic renal progenitors.

For these reasons, we expect Wilms tumor cells to map to fetal cells, especially fetal kidney cells.

Here, we investigate annotations resulting from 4 different strategies:

  • the _processed.rds objects already contain automated annotations computed by the Data Lab:

    • Annotations determined by SingleR, an automated reference-based method Looney et al. 2019.

    • Annotations determined by CellAssign, an automated marker-gene based method Zhang et al. 2019.

  • the label transfers from two fetal references using Azimuth, that have been performed in the previous analysis

    • Using the human fetal atlas as a reference: This is the reference developed by Cao et al. provided by Azimuth for label transfer. The reference contain cells from 15 organs including kidney from fetal samples. The label transfer have been performed in the notebook 02a_fetal_full_reference_Cao_{sample_id}.Rmd

    • Using the human fetal kidney atlas: Stewart et al. created a human fetal kidney atlas. This reference contains only fetal kidney cells and has been precisely annotated by kidney experts. The label transfer have been performed in the notebook 02b_fetal_kidney_reference_Stewart_{sample_id}.Rmd

Annotation from SingleR (no cancer or kidney specific dataset)

visualize_metadata(srat, meta = "singler_celltype_annotation", group.by = "seurat_clusters")
## [1] "seurat_clusters"
## Warning: ggrepel: 20 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Annotation from cellassign (no cancer or kidney specific dataset)

visualize_metadata(srat, meta = "cellassign_celltype_annotation", group.by = "seurat_clusters")
## [1] "seurat_clusters"
## Warning: ggrepel: 20 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Annotation from the fetal reference (Cao et al)

The fetal full reference (Cao et al.) provides three levels of annotations:

  • fetal_full_predicted.organ is one of the 15 organs used for building the reference. We were nicely surprised that, for many of the samples, most of the cells from the Wilms tumor cohort mapped to fetal kidney cells.

  • fetal_full_predicted.annotation.l1 holds predicted cell type annotation

  • fetal_full_predicted.annotation.l2 holds the combination of the predicted organ and predicted cell type annotation

visualize_metadata(srat, meta = "fetal_full_predicted.organ", group.by = "seurat_clusters")
## [1] "seurat_clusters"

tmp <- table(srat$fetal_full_predicted.organ)
percent.of.kidney <- tmp["Kidney"] / sum(tmp) * 100

Of note, 85.4925176 % of the cells are labeled as kidney cells.

visualize_metadata(srat, meta = "fetal_full_predicted.annotation.l1", group.by = "seurat_clusters")
## [1] "seurat_clusters"
## Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

visualize_metadata(srat, meta = "fetal_full_predicted.annotation.l2", group.by = "seurat_clusters")
## [1] "seurat_clusters"
## Warning: ggrepel: 115 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Annotation from the kidney reference (Stewart et al)

The fetal kidney reference (Stewart et al.) provides two levels of annotations:

  • fetal_kidney_predicted.compartment is one of the 4 main compartments composing a fetal kidney. We expect immune and endothelial cells to be healthy (non-canerous) cells identified easily with high confidency. We expect stroma and fetal nephron compartment to contain both normal and malignant cells.

    • immune cells

    • stroma cells

    • fetal_nephron cells

    • endothelial cells

  • fetal_kidney_predicted.cell_type holds predicted cell type annotations that details further the four compartments, as for example:

    • immune subtypes: myeloid cells, lymphocytes, …

    • stroma subtypes: mesenchymal stem cells, fibroblasts, …

    • fetal_nephron subtypes: podocytes, uteric bud, mesenchymal cells (that appear to be cap mesenchyme cells) …

    • endothelial cells

visualize_metadata(srat, meta = "fetal_kidney_predicted.compartment", group.by = "seurat_clusters")
## [1] "seurat_clusters"

tmp <- table(srat$fetal_kidney_predicted.compartment)
percent.of.immune <- 100*tmp["immune"]/sum(tmp)
percent.of.endothelial <- 100*tmp["endothelium"]/sum(tmp)

Of note, 0.8660364 % of the cells are labeled as immune cells and 0.8226098 % of the cells are labeled as endothelial cells.

Let’s have a look at the repartition of cells per patient:

table(srat$sample_id, srat$fetal_kidney_predicted.compartment)
##              
##               endothelium fetal_nephron immune stroma
##   SCPCS000168          23         22319     30    104
##   SCPCS000169          43          5245     38   1068
##   SCPCS000170           8         25999     85    440
##   SCPCS000171           1          1908     16     85
##   SCPCS000172          12          8004     42    716
##   SCPCS000173           5          4524     37    138
##   SCPCS000174           8          3270     20    682
##   SCPCS000175         243          1468     70   1008
##   SCPCS000176          14          1107     25    337
##   SCPCS000177           0          1736      2     24
##   SCPCS000178          14           490      0      1
##   SCPCS000179          25          6196    111     26
##   SCPCS000180           5          3751     16    542
##   SCPCS000181           5          4060      3     90
##   SCPCS000182           4         10933     24    101
##   SCPCS000183          61           800     88   2405
##   SCPCS000184          39          3249     70    176
##   SCPCS000185          66          1327     24    787
##   SCPCS000186           3           648     22     22
##   SCPCS000187           1           650      9     15
##   SCPCS000188          20          3281     68    511
##   SCPCS000189           4          3860      9      1
##   SCPCS000190           0          1276      1      6
##   SCPCS000191           5           454     15    402
##   SCPCS000192           0          1498     27      0
##   SCPCS000193         383          2429    188   3208
##   SCPCS000194         234          7849     83   2646
##   SCPCS000195         244          1217    238   1102
##   SCPCS000196           0          2340     71    610
##   SCPCS000197           4           873      0      3
##   SCPCS000198           2          1160     10      0
##   SCPCS000199           2          1985      7      6
##   SCPCS000200           5          4098     23    261
##   SCPCS000201          11          1738     32    544
##   SCPCS000202           0           396     18    309
##   SCPCS000203          13          2225      7     49
##   SCPCS000204          23          3374     47    127
##   SCPCS000205          92          9663     76    826
##   SCPCS000206           8          7703     48     17
##   SCPCS000208          18         12078     35    379
visualize_metadata(srat, meta = "fetal_kidney_predicted.cell_type", group.by = "seurat_clusters")
## [1] "seurat_clusters"
## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Compare annotations with marker genes

“fetal_kidney_predicted.compartment”

Here we evaluate with marker genes the identification of endothelial, immune cells and other cancer and non-cancerous sub-populations.

markers = list("malignant" = CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "malignant")],
            "immune"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "immune")],
            "endothelium"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "endothelium")],
            "non-malignant"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "non-malignant")])

SCpubr::do_DotPlot(sample = srat,
                         group.by =  "fetal_kidney_predicted.compartment",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE, 
                         plot.title = "fetal_kidney_predicted.compartment"
                         )

Endothelial cells

We look at the endothelial marker “ENSG00000110799” = “VWF”

visualize_feature(object = srat,
                  features = "ENSG00000110799",
                  group.by = "fetal_kidney_predicted.compartment")

Immune cells

We look at the immune marker “ENSG00000081237” = “PTPRC” alias “CD45”

visualize_feature(object = srat,
                  features = "ENSG00000081237",
                  group.by = "fetal_kidney_predicted.compartment")

#### “fetal_kidney_predicted.cell_type”

SCpubr::do_DotPlot(sample = srat,
                         group.by =  "fetal_kidney_predicted.cell_type",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title = "fetal_kidney_predicted.cell_type"
                          )

#### “fetal_full_predicted.annotation.l1”

SCpubr::do_DotPlot(sample = srat,
                         group.by =   "fetal_full_predicted.annotation.l1",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title =  "fetal_full_predicted.annotation.l1"
                          )

#### “cellassign_celltype_annotation”

SCpubr::do_DotPlot(sample = srat,
                         group.by =  "cellassign_celltype_annotation",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title = "cellassign_celltype_annotation"
                          )

Compare annotations between them

Annotation versus Seurat clusters

Here we check wether one (or more) Seurat cluster do correspond to one label. This would allow us to be more confident in the annotation of immune or endothelial cells for example.

p1 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.compartment",
                            first_group = "seurat_clusters"
                             
                            )


p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_full_predicted.annotation.l1",
                            first_group = "seurat_clusters"
                            )


p3 <- do_Table_Heatmap( srat,
                            last_group = "singler_celltype_annotation",
                            first_group = "seurat_clusters"
                            )


p4 <- do_Table_Heatmap( srat,
                            last_group = "cellassign_celltype_annotation",
                            first_group = "seurat_clusters"
                            )

(p1|p2)/(p3|p4)

Comparison of the labels from the two fetal references

first level annotation
p1 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.compartment",
                            first_group = "fetal_full_predicted.annotation.l1"
                             
                            )
p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.cell_type",
                            first_group = "fetal_full_predicted.annotation.l1"
                          
                            )
p1|p2

second level annotation
p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.cell_type",
                            first_group = "fetal_full_predicted.annotation.l2"
                             
                            )
p2

Comparison of the fetal reference with singleR and cellassign

first level annotation
p1 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.compartment",
                            first_group = "cellassign_celltype_annotation"
                             
                            )
p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.compartment",
                            first_group = "singler_celltype_annotation"
                          
                            )
p1|p2

second level annotation
p1 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.cell_type",
                            first_group = "cellassign_celltype_annotation"
                      
                            )

p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.cell_type",
                            first_group = "singler_celltype_annotation"
                      
                            )

p1/p2

Session Info

# record the versions of the packages used in this analysis and other environment information
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] org.Hs.eg.db_3.19.1  AnnotationDbi_1.66.0 IRanges_2.38.1      
##  [4] S4Vectors_0.42.1     Biobase_2.64.0       BiocGenerics_0.50.0 
##  [7] reshape2_1.4.4       patchwork_1.3.0      lubridate_1.9.3     
## [10] forcats_1.0.0        stringr_1.5.1        dplyr_1.1.4         
## [13] purrr_1.0.2          readr_2.1.5          tidyr_1.3.1         
## [16] tibble_3.2.1         ggplot2_3.5.1        tidyverse_2.0.0     
## [19] SCpubr_2.0.2         sctransform_0.4.1    Seurat_5.1.0        
## [22] SeuratObject_5.0.2   sp_2.1-4            
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.22        splines_4.4.1           later_1.3.2            
##   [4] ggplotify_0.1.2         polyclip_1.10-7         fastDummies_1.7.4      
##   [7] lifecycle_1.0.4         rprojroot_2.0.4         globals_0.16.3         
##  [10] lattice_0.22-6          vroom_1.6.5             MASS_7.3-61            
##  [13] crosstalk_1.2.1         magrittr_2.0.3          plotly_4.10.4          
##  [16] sass_0.4.9              rmarkdown_2.28          jquerylib_0.1.4        
##  [19] yaml_2.3.10             httpuv_1.6.15           spam_2.10-0            
##  [22] spatstat.sparse_3.1-0   reticulate_1.39.0       cowplot_1.1.3          
##  [25] pbapply_1.7-2           DBI_1.2.3               RColorBrewer_1.1-3     
##  [28] abind_1.4-8             zlibbioc_1.50.0         Rtsne_0.17             
##  [31] yulab.utils_0.1.7       GenomeInfoDbData_1.2.12 ggrepel_0.9.6          
##  [34] irlba_2.3.5.1           listenv_0.9.1           spatstat.utils_3.1-0   
##  [37] goftest_1.2-3           RSpectra_0.16-2         spatstat.random_3.3-2  
##  [40] fitdistrplus_1.2-1      parallelly_1.38.0       leiden_0.4.3.1         
##  [43] codetools_0.2-20        DT_0.33                 tidyselect_1.2.1       
##  [46] UCSC.utils_1.0.0        farver_2.1.2            viridis_0.6.5          
##  [49] matrixStats_1.4.1       spatstat.explore_3.3-2  jsonlite_1.8.9         
##  [52] progressr_0.14.0        ggridges_0.5.6          survival_3.7-0         
##  [55] tools_4.4.1             ica_1.0-3               Rcpp_1.0.13            
##  [58] glue_1.7.0              gridExtra_2.3           xfun_0.47              
##  [61] GenomeInfoDb_1.40.1     withr_3.0.1             fastmap_1.2.0          
##  [64] fansi_1.0.6             digest_0.6.37           timechange_0.3.0       
##  [67] R6_2.5.1                mime_0.12               gridGraphics_0.5-1     
##  [70] colorspace_2.1-1        scattermore_1.2         tensor_1.5             
##  [73] spatstat.data_3.1-2     RSQLite_2.3.7           utf8_1.2.4             
##  [76] generics_0.1.3          data.table_1.16.0       httr_1.4.7             
##  [79] htmlwidgets_1.6.4       uwot_0.2.2              pkgconfig_2.0.3        
##  [82] gtable_0.3.5            blob_1.2.4              lmtest_0.9-40          
##  [85] XVector_0.44.0          htmltools_0.5.8.1       dotCall64_1.1-1        
##  [88] scales_1.3.0            png_0.1-8               spatstat.univar_3.0-1  
##  [91] knitr_1.48              rstudioapi_0.16.0       tzdb_0.4.0             
##  [94] nlme_3.1-166            cachem_1.1.0            zoo_1.8-12             
##  [97] KernSmooth_2.23-24      parallel_4.4.1          miniUI_0.1.1.1         
## [100] pillar_1.9.0            grid_4.4.1              vctrs_0.6.5            
## [103] RANN_2.6.2              promises_1.3.0          xtable_1.8-4           
## [106] cluster_2.1.6           evaluate_1.0.0          cli_3.6.3              
## [109] compiler_4.4.1          rlang_1.1.4             crayon_1.5.3           
## [112] future.apply_1.11.2     labeling_0.4.3          plyr_1.8.9             
## [115] fs_1.6.4                stringi_1.8.4           viridisLite_0.4.2      
## [118] deldir_2.0-4            assertthat_0.2.1        munsell_0.5.1          
## [121] Biostrings_2.72.1       lazyeval_0.2.2          spatstat.geom_3.3-3    
## [124] Matrix_1.7-0            RcppHNSW_0.6.0          hms_1.1.3              
## [127] bit64_4.5.2             future_1.34.0           KEGGREST_1.44.1        
## [130] shiny_1.9.1             highr_0.11              ROCR_1.0-11            
## [133] igraph_2.0.3            memoise_2.0.1           bslib_0.8.0            
## [136] bit_4.5.0